insightface/benchmarks/train/nvidia_rtx3090.md

Training performance report on NVIDIA RTX3090

GEFORCE RTX 3090 The GeForce RTX™ 3090 is a big ferocious GPU (BFGPU) with TITAN class performance.

Besides, we can also use GeForce RTX™ 3090 to train deep learning models by its FP16 and TF32 supports.

Test Server Spec

Key	Value
CPU	2 x Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
Memory	384GB
GPU	8 x GeForce RTX™ 3090
OS	Ubuntu 18.04.4 LTS
Installation	CUDA 11.1,
Installation	Python 3.7.3
Installation	PyTorch 1.9.0 (pip)

Experiments on arcface_torch

We report training speed in following table, please also note that:

1. The training dataset is SyntheticDataset.

2. Embedding-size are all set to 512.

1. 2 Million Identities

We use a large dataset which contains about 2 millions identities to simulate real cases.

Dataset	Classes	Backbone	Batch-size	FP16	TF32	Partial FC	Samples/sec
WebFace40M	2 Millions	IResNet-50	512	×	×	×	~1750
WebFace40M	2 Millions	IResNet-50	512	×	√	×	~1810
WebFace40M	2 Millions	IResNet-50	512	√	√	×	~2056
WebFace40M	2 Millions	IResNet-50	512	√	√	√	~2850
WebFace40M	2 Millions	IResNet-50	1024	√	√	×	~2810
WebFace40M	2 Millions	IResNet-50	1024	√	√	√	~4220
WebFace40M	2 Millions	IResNet-50	2048	√	√	√	~5330

2. 600K Identities

We use a large dataset which contains about 600k identities to simulate real cases.

Dataset	Classes	Backbone	Batch-size	FP16	Samples/sec
WebFace600K	618K	IResNet-50	512	×	~2220
WebFace600K	618K	IResNet-50	512	√	~2610
WebFace600K	618K	IResNet-50	1024	×	~2940
WebFace600K	618K	IResNet-50	1024	√	~3790
WebFace600K	618K	IResNet-50	2048	√	~4680